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04483nam a2200301Ia 4500 |
| 001 - CONTROL NUMBER |
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90691 |
| 003 - CONTROL NUMBER IDENTIFIER |
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ft7853 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20251124123643.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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240717n 000 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Description conventions |
rda |
| 041 ## - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
engtag |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
QA267 A73 2024 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
. |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Eunna Jazrel M. Arcilla, Patricia Mae A. Samson. |
| 245 #0 - TITLE STATEMENT |
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| Title |
Enhancement of support vector machine utilizing Roberta applied to sentiment analysis of facebook data. |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
. |
| Name of producer, publisher, distributor, manufacturer |
. |
| Date of production, publication, distribution, manufacture, or copyright notice |
2024 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Dimensions |
. |
| Other physical details |
Undergraduate Thesis: (Bachelor pf Science in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2024. |
| 336 ## - CONTENT TYPE |
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| Content type code |
. |
| Content type term |
text |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
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0 |
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. |
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unmediated |
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rdamedia |
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0 |
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. |
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volume |
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rdacarrier |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
ABSTRACT: Many people are using social media sites lie Facebook to express their opinions, experiences. Or whatever they want to post online. Understanding user sentiment has become crucial for various applications, ranging from marketing to public opinion analysis. Researchers use natural language processing (NLP) and machine learning algorithms to evaluate textual information from Facebook posts and classify sentiments as positive, negative, or neutral. This study delves into sentiment analysis of Facebook data to better understand how users express their emotions. Additionally, the method addresses the limitations of sentiment analysis on social media due to informal language, slang, and context-dependent phrases. The study aims to develop an enhanced Support Vector Machine algorithm for sentiment analysis of Facebook data by utilizing the RoBERTa ( A Robustly optimized BERT) model. To enhanced sentiment accuracy, thus the performance of the traditional SVM algorithm, the proposed approach uses VADER to predict initial sentiment labels, loads a pre-trained RoBERTa model as preprocessing techniques, fine-tunes the RoBERTa model and extracts RoBERTa embeddings to optimize the SVM algorithm. This improves the model’s capacity to handle imbalanced datasets and efficiently manage larger datasets while filtering out noisy or irrelevant characteristics. To analyse the performance of the proposed technique, results are compared with the result of existing algorithms. The enhanced SVM algorithm significantly outperforms the existing approach in terms of accuracy, precision, recall, and F1-score, with a 4% to 8% improvement in accuracy over the previous algorithm. This research highlights the potential of integrating RoBERTa techniques with SVM for enhanced sentiment analysis. |
| 506 ## - RESTRICTIONS ON ACCESS NOTE |
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| Terms governing access |
5 |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
ABSTRACT: Many people are using social media sites lie Facebook to express their opinions, experiences. Or whatever they want to post online. Understanding user sentiment has become crucial for various applications, ranging from marketing to public opinion analysis. Researchers use natural language processing (NLP) and machine learning algorithms to evaluate textual information from Facebook posts and classify sentiments as positive, negative, or neutral. This study delves into sentiment analysis of Facebook data to better understand how users express their emotions. Additionally, the method addresses the limitations of sentiment analysis on social media due to informal language, slang, and context-dependent phrases. The study aims to develop an enhanced Support Vector Machine algorithm for sentiment analysis of Facebook data by utilizing the RoBERTa ( A Robustly optimized BERT) model. To enhanced sentiment accuracy, thus the performance of the traditional SVM algorithm, the proposed approach uses VADER to predict initial sentiment labels, loads a pre-trained RoBERTa model as preprocessing techniques, fine-tunes the RoBERTa model and extracts RoBERTa embeddings to optimize the SVM algorithm. This improves the model's capacity to handle imbalanced datasets and efficiently manage larger datasets while filtering out noisy or irrelevant characteristics. To analyse the performance of the proposed technique, results are compared with the result of existing algorithms. The enhanced SVM algorithm significantly outperforms the existing approach in terms of accuracy, precision, recall, and F1-score, with a 4% to 8% improvement in accuracy over the previous algorithm. This research highlights the potential of integrating RoBERTa techniques with SVM for enhanced sentiment analysis. |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
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| Classification |
Filipiniana |
| 540 ## - TERMS GOVERNING USE AND REPRODUCTION NOTE |
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| Terms governing use and reproduction |
5 |
| 655 ## - INDEX TERM--GENRE/FORM |
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| Genre/form data or focus term |
academic writing |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Institution code [OBSOLETE] |
lcc |
| Item type |
Thesis/Dissertation |
| Koha issues (borrowed), all copies |
1 |
| Source of classification or shelving scheme |
|